1.6.3 Types of Study Design
The summary below provides a brief overview of some of the types of study designs available. There are many more designs, of course, that address complex issues of multiple factors influencing multiple outcomes, with corresponding statistical analysis, but these are dealt with in more advanced textbooks on research design and analysis. The list below repeats some of the material covered in Section 1.2 on logic, but goes into more detail in relation to study design.
The principle aim is to conduct studies that are free from bias and based on relevant measures of exposure and outcome so that the hypothesis can be tested effectively.
Observational Studies
Observational studies usually focus on the characteristics or distribution of phenomena in the population that you are investigating. Such studies may analyze data at one point in time or explore time trends in the relevant variables. They may be based on observations of individuals within a sample, or they may consider the relationship between variables observed in groups of subjects (for example, differences in diet and disease rate between countries). They are often the basis for hypothesis generating, rather than hypothesis testing.
Case studies are reports of potentially generalizable or particularly interesting phenomena. Individually, a case study cannot provide evidence that will help you to establish the truth of your hypothesis. Consistent findings across several case studies may provide support for an idea, but cannot be used in themselves to test a hypothesis.
Descriptive studies are careful analyses of the distribution of phenomena within or between groups, or a study of relationships existing between two or more variables within a sample. Descriptive studies are often well suited to qualitative examination of a problem (e.g. examining the coping strategies used by families on low income to ensure an adequate diet for their children when other demands [like the gas bill] are competing for limited cash). But of course they also provide descriptions of quantitative observations for single variables (e.g. how much money is spent on food, fuel, etc. in families on low income), or multiple variables (e.g. how money is spent on food in relation to total income or family size). Many epidemiological studies fall into this category (see below). They are useful for understanding the possible links between phenomena, but cannot in themselves demonstrate cause and effect.
Diagnostic studies establishing the extent of variation in disease states. They are helpful when selecting subjects for a study and deciding on which endpoints may be relevant when designing a study to explore cause and effect.
Experimental and Intervention Studies
These studies are designed to create differences in exposure to a factor which is believed to influence a particular outcome, for example, the effect of consuming oat bran on serum cholesterol levels, or the effect on birth weight of introducing an energy supplement during pregnancy. The aim is usually to analyze the differences in outcome associated with the variations in exposure which have been introduced, holding constant other factors which could also affect the outcome.
These types of studies are usually prospective or longitudinal in design. Alternatively, they may make use of existing data. Depending on how subjects are selected, they may use inductive or deductive logic to draw their conclusions (see Section 1.2)
Pre‐test–post‐test (Figure 1.1). This is the simplest (and weakest) of the prospective experimental designs. There is one sample. It may be ‘adventitious’ – subjects are selected as they become available (for example, a series of patients coming into a diabetic clinic, or a series of customers using a particular food shop); or it may be ‘purposive’ (the sample is drawn systematically from a population using techniques that support generalization of the findings). Each individual is measured at the start of the study (the ‘baseline’ or ‘time zero’). There is then an intervention. Each subject is measured again at the end of the study.
FIGURE 1.1 Pre‐test–post‐test design.
The weakness of this design is that there may have been a number of factors influencing the outcome of the study, but you may be aware of only some of them. An example is a study on the influence on growth of food supplements given to children. Was there bias in the way the children were recruited into the study? Did the supplement contain enough of the relevant energy or nutrients to have an impact on growth? Were the children at the right age for the supplement to have an impact? Was follow‐up long enough to observe the impact of the supplement on growth? Participation in the study may in itself benefit growth (more social interaction, more stimulation from adults and peers, more food sharing, changing dietary patterns at home as a result of parental involvement in the study, etc.). It is possible that the direct effect of the supplement may represent only one of several factors that can have in impact on growth.
One sample (Figure 1.2). Here, two or more treatments are compared in one group of subjects. Each subject acts as their own control. There may be a placebo being compared with an active treatment.
The danger here is that the order of administration may have an effect on the outcome. It is therefore desirable to randomize the order of administration of the treatment and placebo (making the design more like a cross‐over clinical trial – see below). Thus, for one subject, Intervention 1 will be the active treatment, and Intervention 2 the placebo; for another subject, Intervention 1 will be the placebo, and Intervention 2 the active treatment. A ‘wash out’ period may be required, so that any residual effect of the first treatment has time to disappear and not appear as an influence on the second treatment. This may not be possible where a psychological or attitudinal variable is being measured, because the views of a subject may be permanently influenced once they have started to participate in the study.
FIGURE 1.2 One‐sample design.
Two or more samples, unmatched (Figure 1.3). Similar experimental groups are given different treatments. One group acts as a control (or placebo) group.
This is a stronger experimental design than the pre‐test–post‐test or one‐sample designs. However, the groups may differ in some characteristic which is important to the outcome (for example they may differ in age). Even if the groups are not ‘matched’ exactly, some attempt should be made to ensure that the groups are similar regarding variables which may be related to the outcome. For example, in a study to see if the administration of oral zinc supplements improves taste sensitivity in cancer patients, the type, degree, and severity of disease would need to be
